Research Advisor - Computational RNA Genomics

Role Summary

The Research Advisor will bridge computational genomics and RNA biology to generate insights that directly drive genetic medicine programs forward. It sits within a dynamic team applying deep sequencing and bioinformatic approaches to advance our therapeutic pipeline. The successful candidate will lead the computational analysis of RNA biology datasets to inform therapeutic development and advance understanding of genetic mechanisms. Lilly Genetic Medicine is headquartered in Indianapolis, Indiana.

Responsibilities

• Process and analyze deep sequencing datasets including RNA-seq, ribosome profiling, CLIP-seq, SHAPE-seq and other transcriptome-profiling technologies to investigate mRNA processing, stability, localization, translation, and decay
• Develop, optimize, and implement computational pipelines and workflows for analyzing complex RNA sequencing data
• Interpret analytical results within the context of RNA biology, disease mechanisms, and therapeutic applications
• Collaborate with experimental biologists, molecular biologists, and therapeutic project teams to design studies, prioritize experiments, and validate computational findings
• Present findings to cross-functional teams and contribute to scientific strategy discussions
• Contribute to peer-reviewed publications, patent applications, and scientific presentations at conferences
• Stay current with emerging technologies and methodologies in RNA biology and computational genomics

Qualifications

• Required: PhD in Bioinformatics, Computational Biology, Molecular Biology, Genetics, or related field
• Preferred: Demonstrated expertise in mRNA processing and RNA biology
• Preferred: Prior hands-on experience with transcriptome profiling and analysis of RNA-seq datasets
• Preferred: Proficiency in computational analysis of deep sequencing data
• Preferred: Strong programming skills in Python, R, or similar languages
• Preferred: Experience with standard bioinformatics tools, pipelines, and databases for genomic analysis
• Preferred: Excellent written and oral communication skills with ability to present complex data to diverse audiences
• Preferred: Demonstrated ability to work collaboratively in cross-functional team environments
• Preferred: Experience with diverse RNA sequencing modalities beyond standard RNA-seq (e.g., ribosome profiling, CLIP-seq, single-cell RNA-seq, SHAPE-seq, spatial transcriptomics)
• Preferred: Knowledge of therapeutic RNA modalities or genetic medicine applications
• Preferred: Experience with cloud computing platforms and high-performance computing environments
• Preferred: Familiarity with machine learning approaches applied to genomic data
• Preferred: Track record of peer-reviewed publications in high-impact journals demonstrating expertise in RNA biology and/or computational genomics
• Preferred: Experience with statistical modeling and experimental design
• Preferred: Knowledge of drug discovery and development processes
• Preferred: Experience contributing to reusable data assets and standardized pipelines that support cross-program integration and downstream machine learning applications

Skills

• Demonstrated expertise in mRNA processing and RNA biology
• Prior hands-on experience with transcriptome profiling and analysis of RNA-seq datasets
• Proficiency in computational analysis of deep sequencing data
• Strong programming skills in Python, R, or similar languages
• Experience with standard bioinformatics tools, pipelines, and databases for genomic analysis
• Excellent written and oral communication skills with ability to present complex data to diverse audiences
• Demonstrated ability to work collaboratively in cross-functional team environments
• Experience with diverse RNA sequencing modalities beyond standard RNA-seq (e.g., ribosome profiling, CLIP-seq, single-cell RNA-seq, SHAPE-seq, spatial transcriptomics)
• Knowledge of therapeutic RNA modalities or genetic medicine applications
• Experience with cloud computing platforms and high-performance computing environments
• Familiarity with machine learning approaches applied to genomic data
• Track record of peer-reviewed publications in high-impact journals demonstrating expertise in RNA biology and/or computational genomics
• Experience with statistical modeling and experimental design
• Knowledge of drug discovery and development processes
• Experience contributing to reusable data assets and standardized pipelines that support cross-program integration and downstream machine learning applications

Additional Requirements

• Occasional travel for conferences and collaborations